Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM
Improper regulation of sugarcane feed volume can lead to harvester inefficiency or clogging. Accurate recognition of feed volume is therefore critical. However, visual recognition is challenging due to sugarcane stacking during feeding. To address this, we propose YOLO-ASM (YOLO Accurate Stereo Matc...
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MDPI AG
2025-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/13/1428 |
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| author | Xiao Lai Guanglong Fu |
| author_facet | Xiao Lai Guanglong Fu |
| author_sort | Xiao Lai |
| collection | DOAJ |
| description | Improper regulation of sugarcane feed volume can lead to harvester inefficiency or clogging. Accurate recognition of feed volume is therefore critical. However, visual recognition is challenging due to sugarcane stacking during feeding. To address this, we propose YOLO-ASM (YOLO Accurate Stereo Matching), a novel detection method. At the target detection level, we integrate a Convolutional Block Attention Module (CBAM) into the YOLOv5s backbone network. This significantly reduces missed detections and low-confidence predictions in dense stacking scenarios, improving detection speed by 28.04% and increasing mean average precision (mAP) by 5.31%. At the stereo matching level, we enhance the SGBM (Semi-Global Block Matching) algorithm through improved cost calculation and cost aggregation, resulting in Opti-SGBM (Optimized SGBM). This double-cost fusion approach strengthens texture feature extraction in stacked sugarcane, effectively reducing noise in the generated depth maps. The optimized algorithm yields depth maps with smaller errors relative to the original images, significantly improving depth accuracy. Experimental results demonstrate that the fused YOLO-ASM algorithm reduces sugarcane volume error rates across feed volumes of one to six by 3.45%, 3.23%, 6.48%, 5.86%, 9.32%, and 11.09%, respectively, compared to the original stereo matching algorithm. It also accelerates feed volume detection by approximately 100%, providing a high-precision solution for anti-clogging control in sugarcane harvester conveyor systems. |
| format | Article |
| id | doaj-art-e1474da04fab4f9c8c0e880219355908 |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-e1474da04fab4f9c8c0e8802193559082025-08-20T03:17:52ZengMDPI AGAgriculture2077-04722025-07-011513142810.3390/agriculture15131428Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASMXiao Lai0Guanglong Fu1School of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaSchool of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaImproper regulation of sugarcane feed volume can lead to harvester inefficiency or clogging. Accurate recognition of feed volume is therefore critical. However, visual recognition is challenging due to sugarcane stacking during feeding. To address this, we propose YOLO-ASM (YOLO Accurate Stereo Matching), a novel detection method. At the target detection level, we integrate a Convolutional Block Attention Module (CBAM) into the YOLOv5s backbone network. This significantly reduces missed detections and low-confidence predictions in dense stacking scenarios, improving detection speed by 28.04% and increasing mean average precision (mAP) by 5.31%. At the stereo matching level, we enhance the SGBM (Semi-Global Block Matching) algorithm through improved cost calculation and cost aggregation, resulting in Opti-SGBM (Optimized SGBM). This double-cost fusion approach strengthens texture feature extraction in stacked sugarcane, effectively reducing noise in the generated depth maps. The optimized algorithm yields depth maps with smaller errors relative to the original images, significantly improving depth accuracy. Experimental results demonstrate that the fused YOLO-ASM algorithm reduces sugarcane volume error rates across feed volumes of one to six by 3.45%, 3.23%, 6.48%, 5.86%, 9.32%, and 11.09%, respectively, compared to the original stereo matching algorithm. It also accelerates feed volume detection by approximately 100%, providing a high-precision solution for anti-clogging control in sugarcane harvester conveyor systems.https://www.mdpi.com/2077-0472/15/13/1428sugarcane harvesterdeep learningfeed volume detectionalgorithm fusion |
| spellingShingle | Xiao Lai Guanglong Fu Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM Agriculture sugarcane harvester deep learning feed volume detection algorithm fusion |
| title | Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM |
| title_full | Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM |
| title_fullStr | Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM |
| title_full_unstemmed | Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM |
| title_short | Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM |
| title_sort | sugarcane feed volume detection in stacked scenarios based on improved yolo asm |
| topic | sugarcane harvester deep learning feed volume detection algorithm fusion |
| url | https://www.mdpi.com/2077-0472/15/13/1428 |
| work_keys_str_mv | AT xiaolai sugarcanefeedvolumedetectioninstackedscenariosbasedonimprovedyoloasm AT guanglongfu sugarcanefeedvolumedetectioninstackedscenariosbasedonimprovedyoloasm |